Derek T. Robinson

Comparison of empirical methods for building agent-based models in land use science

The use of agent-based models (ABMs) for investigating land-use science questions has been increasing dramatically over the last decade. Modelers have moved from ‘proofs of existence’ toy models to case-specific, multi-scaled, multi-actor, and data-intensive models of land-use and land-cover change. An international workshop, titled ‘Multi-Agent Modeling and Collaborative Planning—Method2Method Workshop’, was held in Bonn in 2005 in order to bring together researchers using different data collection approaches to informing agent-based models. Participants identified a typology of five approaches to empirically inform ABMs for land use science: sample surveys, participant observation, field and laboratory experiments, companion modeling, and GIS and remotely sensed data. This paper reviews these five approaches to informing ABMs, provides a corresponding case study describing the model usage of these approaches, the types of data each approach produces, the types of questions those data can answer, and an evaluation of the strengths and weaknesses of those data for use in an ABM.

Effects of Heterogeneity in Residential Preferences on an Agent-Based Model of Urban Sprawl

The ability of agent-based models (ABMs) to represent heterogeneity in the characteristics and behaviors of actors enables analyses about the implications of this heterogeneity for system behavior. The importance of heterogeneity in the specification of ABMs, however, creates new demands for empirical support. An earlier analysis of a survey of residential preferences within southeastern Michigan revealed seven groups of residents with similar preferences on similar characteristics of location. In this paper, we present an ABM that represents the process of residential development within an urban system and run it for a hypothetical pattern of environmental variation. Residential locations are selected by residential agents, who evaluate locations on the basis of preference for nearness to urban services, including jobs, aesthetic quality of the landscape, and their similarity to their neighbors. We populate our ABM with a population of residential preferences drawn from the survey results in five different ways: (1) preferences drawn at random; (2) equal preferences based on the mean from the entire survey sample; (3) preferences drawn from a single distribution, whose mean and standard deviation are derived from the survey sample; (4) equal preferences within each of seven groups, based on the group means; and (5) preferences drawn from distributions for each of seven groups, defined by group means and standard deviations. Model sensitivity analysis, based on multiple runs of our model under each case, revealed that adding heterogeneity to agents has a significant effect on model outcomes, measured by aggregate patterns of development sprawl and clustering.

Colonist household decisionmaking and land-use change in the Amazon Rainforest: an agent-based simulation

An agent-based model was developed as a tool designed to explore our understanding of spatial, social, and environmental issues related to land-use/cover change. The model focuses on a study site in a region of the Amazon frontier, characterized by the development of family farms on 100-ha lots arranged along the Transamazon highway and a series of side roads, west of Altamira, Brazil. The model simulates the land-use behaviour of farming households on the basis of a heuristic decisionmaking strategy that utilizes burn quality, subsistence requirements, household characteristics, and soil quality as key factors in the decisionmaking process. Farming households interact through a local labour pool. The effects of the land-use decisions made by households affect the land cover of their plots and ultimately that of the region. This paper describes this model, referred to as LUCITA, and presents preliminary results showing land-cover changes that compare well with observed land-use and land-cover changes in the region.

From actors to agents in socio-ecological systems models

The ecosystem service concept has emphasized the role of people within socio-ecological systems (SESs). In this paper, we review and discuss alternative ways of representing people, their behaviour and decision-making processes in SES models using an agent-based modelling (ABM) approach. We also explore how ABM can be empirically grounded using information from social survey. The capacity for ABM to be generalized beyond case studies represents a crucial next step in modelling SESs, although this comes with considerable intellectual challenges. We propose the notion of human functional types, as an analogy of plant functional types, to support the expansion (scaling) of ABM to larger areas. The expansion of scope also implies the need to represent institutional agents in SES models in order to account for alternative governance structures and policy feedbacks. Further development in the coupling of human-environment systems would contribute considerably to better application and use of the ecosystem service concept.

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Effects of land markets and land management on ecosystem function: A framework for modelling exurban land-change

This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux. To demonstrate the framework, we implement a simple integration of a new agent-based model of exurban residential development and land-management decisions with the ecosystem process model BIOME-BGC. Using a stylized scenario, we evaluate the influence of different exurban residential-land-management strategies on carbon storage at the parcel level over a 48-year period from 1958 to 2005, simulating stocks of carbon in soil, litter, vegetation, and net primary productivity. Results show 1) residential parcels with management practices that only provided additions in the form of fertilizer and irrigation to turfgrass stored slightly more carbon than parcels that did not include management practices, 2) conducting no land-management strategy stored more carbon than implementing a strategy that included removals in the form of removing coarse woody debris from dense tree cover and litter from turfgrass, and 3) the removal practices modelled had a larger impact on total parcel carbon storage than our modelled additions. The degree of variation within the evaluated land-management practices was approximately 42,104?kg?C storage on a 1.62?ha plot after 48 years, demonstrating the substantial effect that residential land-management practices can have on carbon storage. Highlights? The new framework integrates agent-based and ecosystem models to link management and the C cycle. ? Fertilization and irrigation of residential turfgrass stored more carbon than no management. ? No management stored more carbon than managements removing woody debris and grass clippings. ? Removal practices had a larger impact on total parcel carbon storage than modeled additions. ? Residential land-management practices can have a substantial effect on carbon storage.

Do Land Markets Matter? A Modeling Ontology and Experimental Design to Test the Effects of Land Markets for an Agent-Based Model of Ex-Urban Residential Land-Use Change

Urban sprawl is shaped by various geographical, ecological and social factors under the influence of land market forces. When modeling this process, geographers and economists tend to prioritize factors most relevant to their own domain. Still, there are very few structured systematic comparisons exploring how the extent of process representation affects the models’ ability to generate extent and pattern of change. This chapter aims to explore the question of how the degree of representation of land market processes affects simulated spatial outcomes. We identify four distinct elements of land markets: resource constraints, competitive bidding, strategic behavior, and endogenous supply decisions. Many land-use-change models include one or more of these elements; thus, the progression that we designed should facilitate analysis of our results in relation to a broad range of existing land-use-change models, from purely geographic to purely economic and from reduced form to highly structural models. The description of the new agent-based model, in which each of the four levels of market representation can be gradually activated, is presented. The behavior of suppliers and acquirers of land, and the agents’ interactions at land exchange are discussed in the presence of each of the four land-market mechanisms.

Market impacts on land-use change: an agent-based experiment

Land-use change in a market economy, particularly at the urban–rural fringe in North America, is shaped through land and housing markets. Although market activities are at the core of economic studies of land-use change, many market elements are neglected by coupled human–environment models. We scrutinized the effects of the level of detail of market representation using an abstract, agent-based model of land-use change. This model includes agents representing land buyers and sellers and their respective market-based decision-making behaviors. Our results show that although incorporating key market elements, particularly budget constraints and competitive bidding, in land-use models generally alters projected land-use patterns, their impacts differ significantly depending on the level of detail of market representation. Consistent with theories of land change, our research confirms that budget constraints can considerably reduce the projected quantity of land-use change. The effects of competitive bidding, however, are more complex and depend on buyers’ budgets, their relative preferences for proximity versus open-space amenities, and the size of neighborhoods. Market competition might reduce or increase the quantity of land-use change and the degree of sprawl in the simulated landscapes. Because of the strong effects of market elements on resulting patterns, adequate representation of the structure of markets is important for capturing and characterizing the complexity inherent in coupled human–environment systems.

Representing ecological processes in agent-based models of land use and cover change

Agent-based models of land use and cover change (ABMs/LUCC) have traditionally represented land-use and land-cover changes as arising from social, economic and demographic conditions, while spatial ecological models have tended to simulate the environmental impacts of spatially aggregated human decisions. Incorporating a dynamic representation of ecosystem processes into ABMs/LUCC can enable new or counter-intuitive insights to be gained into why certain path-dependent outcomes arise and can also spatially constrain model processes, thereby improving the spatial fit of model output against observational data. A framework is therefore provided to assist in determining an optimal approach for representing ecological processes in an ABM/LUCC according to the research question and desired application of the model. Relevant challenges limiting the integration of complex, dynamic representations of ecosystem processes into ABMs/LUCC are then assessed, with solutions provided from recent examples. ABMs/LUCC that use a dynamic representation of ecological processes may be applied to investigate the complex, long-term responses of the coupled human–natural system to a variety of climatic shifts and ecological disturbances.

Modular ABM development for improved dissemination and training

Agent-based models (ABMs) have become an important tool for advancing scientific understanding in a variety of disciplines and more specifically have contributed gains to natural resource management in recent decades. However, a key challenge to their utility is the lack of convergence upon a common set of assumptions for representing key processes (such as agent decision structure), with the outcome that published ABM tools are rarely (if ever) used beyond their original development team. While a number of ABM frameworks are publicly available for use, the continued development of models from scratch is a signal of the continuing difficulty in capturing sufficient modeling flexibility in a single package. In this study we examine ABM sharing by comparing co-citation networks from several well-known ABM frameworks to those used in the land-use change modeling community. We then outline a different publication paradigm for the ABM community that could improve the sharing of model structure and help move toward convergence on a common set of tools and assumptions. Gaps in the reuse and publication of agent-based modeling primitives are exposed.Coauthorship networks of statistical versus agent models differ substantially.Examples of agent-based modeling primitives and options for research are provided.